Abalone, a type of marine mollusk, are valued for their meat and shells, making accurate age determination crucial for sustainable fisheries management. Traditional methods of age determination, involving the manual counting of growth rings on shells, are labor-intensive and prone to error due to variability in ring deposition and shell wear. To address these challenges, this study aims to predict the age of abalone using non-destructive methods based on physical measurements.
The age of an abalone is conventionally estimated by counting growth rings on its shell and adding 1.5 years, as each ring represents approximately one year of growth. Leveraging this relationship, our goal is to develop a robust regression model that accurately predicts abalone age using measurable attributes such as length, diameter, height, and various weights (whole, shucked, viscera, and shell).
By establishing a reliable predictive model, we aim to streamline age estimation processes in abalone fisheries management, contributing to sustainable harvesting practices and conservation efforts.
Data is from UCI machine learning repository. https://archive.ics.uci.edu/dataset/1/abalone